Yanpei Chen, Archana Sulochana Ganapathi and Randy H. Katz

Compression enables us to shift the computation load from IO to CPU. In modern datacenters where energy efficiency is a growing concern, the benefits of using compression have not been completely exploited. We develop a decision algorithm that helps MapReduce users identify when and where to use compression. For some jobs, using compression gives energy savings of up to 60%. As MapReduce represents a common computation framework for Internet datacenters, we believe our findings will provide signficant impact on improving datacenter energy efficiency.

BibTeX citation:

@techreport{Chen:EECS-2010-36,
Author = {Chen, Yanpei and Ganapathi, Archana Sulochana and Katz, Randy H.},
Title = {To Compress or Not To Compress - Compute vs. IO tradeoffs for MapReduce Energy Efficiency},
Institution = {EECS Department, University of California, Berkeley},
Year = {2010},
Month = {Mar},
URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-36.html},
Number = {UCB/EECS-2010-36},
Abstract = {Compression enables us to shift the computation load from IO to CPU. In modern datacenters where energy efficiency is a growing concern, the benefits of using compression have not been completely exploited. We develop a decision algorithm that helps MapReduce users identify when and where to use compression. For some jobs, using compression gives energy savings of up to 60%. As MapReduce represents a common computation framework for Internet datacenters, we believe our findings will provide signficant impact on improving datacenter energy efficiency.}
}